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      <h1 class="title" style="text-align: center">
        Monitoring the changes of the coastline and saline-alkali soil for
        Jiangsu province by fusing multisource remotely sensed data
      </h1>
      <p>
        China has a huge area of saline-alkali soil, among which there is about
        140000 km2 belonging to coastal saline-alkali soil. Jiangsu province is
        the main coastal place in China with about 7000 km2 saline-alkali soil
        locating in Lianyungang and Yancheng cities. Because of changes of
        coastline and rainfall caused by climate change, the situation of the
        saline-alkali soil in Jiangsu, or China, becomes even worse. How to
        monitor the changes of the coastline and saline-alkali soil and their
        relationship precisely concerns the food safety of the whole country.
      </p>
      <p>
        In order to monitor the saline-alkali soil, it is needed to extract the
        coastline, the characteristics of the land, the land cover and mapping
        and the rainfall in large scale and quickly. Under this situation, it is
        the most effective way to realize this purpose by applying multisource
        and multi-temporal remotely sensed data. This project will provide the
        technical framework for monitoring the coastline and saline-alkali soil
        precisely by fusing multisource remotely sensed data, which is scalable
        in application to other coastal areas in China or even in the world.
      </p>
      <section>
        <h3 class="sub-title">
          1. Coastline change extraction from remote sensing images
        </h3>
        <p>
          Remote sensing technology provides effective means for rapid
          extraction of coastline changes. Currently, optical and radar remote
          sensing images with multi-band, high-temporal and high-spatial
          resolution provide rich data sources for coastline extraction. Through
          the Normalized Difference Water Index (NDWI) constructed based on
          green band and near-infrared band for optical remote sensing images
          and the technology of wavelet-based spatial correlator for edge
          detection for SAR images, the coastline detection and extraction from
          time-series optical and radar data are realized adaptively.
        </p>

        <ul>
          <li>
            (1) The specific algorithm steps of extracting waterlines from
            optical images are as follows:
            <ol>
              <li>
                a) Determining the area within a certain range of the boundary
                of water and land through the buffer zone as the calculation
                area for reducing the amount of computation;
              </li>
              <li>b) Obtaining the calculation area;</li>
              <li>
                c) Calculating the NDWI of the clipping area by band math of
                (Green-band-NIR-band)/(Green-band+ NIR-band);
              </li>
              <li>
                d) Using OTSU adaptive threshold to binarization NDWI images and
                retain only the water part;
              </li>
              <li>e) Transforming the water part into surface elements;</li>
              <li>f) Eliminating small areas;</li>
              <li>g) Eliminating the void area;</li>
              <li>
                h) Completing the coastline extraction by the transformation
                from surface elements to line.
              </li>
            </ol>
          </li>
          <li>
            (2) The specific algorithm steps of extracting waterlines from radar
            images are as follows:
            <ol>
              <li>
                a) Using the multitemporal filter to filter SAR images for
                suppressing the coherent speckle noise while preserving the
                spatial features;
              </li>
              <li>b) Using the wavelet transformation to extract waterline;</li>
              <li>
                c) Adopting the morphologic filter to eliminate broken lines for
                refining the extracted waterlines.
              </li>
            </ol>
          </li>
        </ul>
        <p>
          At present, the coastline of Jiangsu province for several years has
          been automatically extracted by using the above method based on
          Landsat satellite and ERS-1/2 SAR images, as well as coastline changes
          in this region have been analyzed.
        </p>
      </section>
      <section>
        <h3 class="sub-title">
          2. Soil salinization inversion and multi-satellite precipitation
          analysis from remotely sensed data
        </h3>
        <p>
          Soil salinization is one of the main reasons for the shortage of
          cultivated land resources and the deterioration of the ecological
          environment. The farmland in coastal areas is eroded by seawater, and
          crops are under salinity stress for a long time. The land use type has
          gradually changed from farmland to sparse surface. At present, the use
          of various indicators in multispectral remote sensing data to
          construct a feature space for the extraction and monitoring of
          salinization information is an advanced method for the research of
          salinization remote sensing monitoring.
        </p>
        <p>
          This project uses methods such as object-oriented classification and
          spatial-temporal change detection to obtain dynamic change information
          of land cover/use in Jiangsu coastal areas by fusing high-resolution
          series of multi-temporal remote sensing images, and determines key
          areas of soil salinization. Combining the high-resolution series of
          multi-spectral remote sensing data and the salt content of field soil
          samples, calculate the soil spectral information and its salt content
          multivariate statistical parameters, analyze the response
          characteristics of the remote sensing multi-spectral bands to the
          degree of soil salt content, and construct the sensitive diagnostic
          spectral band soil salinity index. Using remote sensing near-infrared
          and red bands, the modified soil regulation vegetation index is
          calculated to express the change characteristics of sparse surface
          vegetation in the process of soil salinization. On this basis, a
          two-dimensional feature space of salinity index and modified soil
          vegetation index was constructed to clarify the boundary conditions
          based on the characteristics of low salinity-high coverage vegetation
          and high salinity-low coverage vegetation under different soil
          salinity and vegetation abundance. The salinity-vegetation index
          characteristic curve will be calculated to construct a pixel-scale
          soil salinization monitoring remote sensing index model,
          quantitatively analyze the temporal and spatial distribution of soil
          salinization in the coastal regions of Jiangsu, and provide technical
          support for agricultural management and soil remediation.
        </p>
        <p>
          Multi-satellite precipitation analysis (tropical rainfall measurement
          mission (TRMM) multi-satellite precipitation analysis, TMPA) data
          products are currently the best comprehensive performance and most
          widely used satellite precipitation products. Besides, the surface
          transpiration and evapotranspiration data products that retrieved
          using a variety of satellite remote sensing data (Landsat, MODIS,
          etc.) with high temporal and spatial resolution and accuracy were
          available from the platform of Geospatial Data Cloud.
        </p>
      </section>
      <section>
        <h3 class="sub-title">
          3. The correlation mining for soil quality and its effective factors
        </h3>
        <p>
          This project intends to use the above products as precipitation and
          evapotranspiration data, combined with the inversion of the soil
          salinization and land use cover, to analyze and explore the response
          between soil salinity and rainfall and evapotranspiration in the area
          along the coastline. Especially, the interaction of precipitation and
          evapotranspiration under different seasons, time periods and spatial
          scales will be analyzed. Then, based on the soil quality data, the
          effectiveness of the water-gas cycle on soil leaching and eluting salt
          will be judged.
        </p>
        <p>
          It is planned to conduct a correlation analysis of multiple ecological
          environmental factors and soil salinity in the study area, so as to
          find out the response factors of the study area and salinity.
          Eco-environmental factors mainly include: meteorological factors such
          as rainfall, rainfall duration, surface evapotranspiration, solar
          radiation intensity, sunshine hours, relative humidity, and other
          factors such as the growth and accumulation of surface saline
          vegetation (Elysium vulgaris, Elymus alkaloid), Growth duration,
          optimal growth period, land use thematic information and other
          parameters. Regression analysis will be used to complete the
          estimation and prediction the relationship of soil salinity and the
          environmental factors. Finally, in-depth analysis and excavation of
          the response between water and gas circulation and soil quality along
          the coastline will be realized.
        </p>
        <p>
          The conclusions from this project will provide important scientific
          significance for the comprehensive management of saline-alkali natural
          disasters and the promotion of the sustainable development of
          agricultural production, resulting in significant economic, social and
          ecological environmental benefits.
        </p>
      </section>
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